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ai-health-check

Audit AI features across six dimensions and grade readiness for pre-launch validation.

16|3|Updated Oct 23, 2025
Search Tags:#audit#data-quality#cost-modeling#pre-launch#ai-health-check#failure-ux
One-click install
npx skills add https://github.com/breethomas/bette-think --skill ai-health-check
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-health-check
Source: https://github.com/breethomas/bette-think/tree/main/plugins/bette-think/skills/ai-health-check
Command: npx skills add https://github.com/breethomas/bette-think --skill ai-health-check

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many AI-driven product features ship with critical gaps like missing cost models, poor data quality, no monitoring, and unusable failure UX; this skill enforces a pre-launch audit to prevent shipping those broken features. It helps product teams catch blockers and risks early so launches are safe, affordable, and monitorable.

Core Features & Use Cases

  • Six-dimension audit: Grades Model Selection, Data Quality, Cost Modeling, Production Monitoring, Failure UX, and System Optimization as Ready / Risk / Blocker.
  • Automated guidance: Invokes an implementation auditor agent, surfaces hard questions, provides an overall verdict, and recommends remediation steps.
  • Use case: Run before a sprint release to validate an email composer, recommendation engine, or any ML-driven feature to decide whether to ship or iterate.

Quick Start

Run the ai-health-check by entering the slash command /ai-health-check followed by a concise feature name (for example: /ai-health-check "email composer AI") to receive a graded readiness report.

Frequently Asked Questions about ai-health-check

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit AI features for pre-launch readiness?▼

Pre-launch AI validation audits assess model selection, data quality, cost modeling, monitoring, failure UX, and optimization to assign graded statuses of Ready, Risk, or Blocker before shipping.

What is included in an AI feature readiness audit?▼

AI readiness audits grade six dimensions: model selection, data quality, cost modeling, production monitoring, failure UX, and system optimization, yielding an overall verdict and prescriptive remediation steps.

How do I prevent shipping broken ML-driven UX components?▼

Prevent shipping broken ML-driven UX components by running a pre-launch audit that surfaces hard questions across data quality, cost models, and failure UX to catch blockers and risks early.

Can I use a readiness audit to validate an email composer or recommendation system?▼

Yes, you can validate individual ML-driven features like email composers or recommendation systems by running a pre-launch audit to decide whether to ship or iterate based on graded dimension statuses.

What are the limitations of relying on automated AI health checks?▼

Automated AI health checks are limited to pre-launch validation and may not replace ongoing production monitoring, meaning teams must still continuously track model performance and system optimization post-launch.

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